Craniofacial Defects Persist in <i>Eftud2</i> mutant mice with P53 Deletion
Bibliographic record
Abstract
EFTUD2 is mutated in patients with mandibulofacial dysostosis with microcephaly (MFDM). We previously showed that homozygous deletion of Eftud2 in neural crest cells causes brain and craniofacial malformations, affecting the same precursors as in MFDM patients. This phenotype was partially rescued by inhibition of the P53‐pathway using pifithrin‐α. Herein, we specifically evaluated the role of P53 in craniofacial abnormalities in Eftud2 mutants by genetically removing both Eftud2 and Trp53 in the neural crest cells ( Eftud2‐ P53 double knock‐outs (DKOs). We found that removing one or two alleles of Trp53 did not improve survival or craniofacial development in Eftud2 mutants. However, cleaved caspase‐3 immunostaining revealed that the abnormal increase in cell death found in Eftud2 mutants at E10.5 was reduced in the neural tube of Eftud2‐ P53 neural crest DKOs. This suggests that the craniofacial abnormalities found in Eftud2‐ P53 neural crest DKOs mutants are not only caused by apoptosis. We next aim to evaluate if cell cycle arrest could contribute to the phenotype. FoxM1 is a transcription factor which is essential for G2 progression into mitosis and is regulated by P53. We found increased splicing of FoxM1 in RNAseq experiments from Eftud2 mutants. Interestingly, the expression of the FoxM1 target, Cdc25b , was decreased by removal of P53 in Eftud2 mutants as was that of Cdkn1 a , a P53‐target required for mitosis. Altogether, our data suggest that in the presence of P53, Eftud2 mutants neural crest cells undergo apoptosis, whereas in the absence of this protein, they undergo cell cycle arrest. Ultimately, mis‐splicing leads to malformation in both a P53‐dependent and independent manner, which both ultimately contribute to a loss of neural crest cells and their derivative structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".